Models that represent meaning as high-dimensional numerical vectors-such as latent semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the aggregate language environment (BEAGLE), topic models, global vectors (GloVe), and word2vec-have been introduced as extremely powerful machine-learning proxies for human semantic representations and have seen an explosive rise in popularity over the past 2 decades. However, despite their considerable advancements and spread in the cognitive sciences, one can observe problems associated with the adequate presentation and understanding of some of their features. Indeed, when these models are examined from a cognitive perspective, a number of unfounded arguments tend to appear in the psychological literature. In this article, we review the most common of these arguments and discuss (a) what exactly these models represent at the implementational level and their plausibility as a cognitive theory, (b) how they deal with various aspects of meaning such as polysemy or compositionality, and (c) how they relate to the debate on embodied and grounded cognition. We identify common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models. We clarify and amend these points to provide a theoretical basis for future research and discussions on vector models of semantic representation.

中文翻译：

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从认知角度看语义表达的向量空间模型：常见误解的讨论。

将意义表示为高维数值矢量的模型-例如潜在语义分析（LSA），语言超空间模拟（HAL），聚合语言环境的绑定编码（BEAGLE），主题模型，全局矢量（GloVe）和word2vec -已被引入作为人类语义表示的功能非常强大的机器学习代理，并且在过去的20年中，其普及程度呈爆炸式增长。然而，尽管它们在认知科学中取得了长足的进步并得到了广泛应用，但人们仍可以观察到与适当呈现和理解其某些特征有关的问题。确实，当从认知的角度考察这些模型时，心理学文献中往往会出现许多毫无根据的论点。在本文中，我们回顾了这些论点中最常见的论点，并讨论了（a）这些模型在实现水平上确切代表什么以及它们作为认知理论的合理性，（b）它们如何处理含义的各个方面，例如多义性或构成性，以及（c ）它们与关于体现和扎实的认知的辩论有何关系。我们确定了由于不完整的描述，过时的论点以及模型的理论和实现之间的不清楚而引起的常见误解。我们澄清和修改这些观点，为将来的语义表示向量模型研究和讨论提供理论依据。（c）它们与关于具体化和扎根认知的辩论有何关系。我们确定了由于不完整的描述，过时的论点以及模型的理论和实现之间的不清楚而引起的常见误解。我们澄清和修改这些观点，为将来的语义表示向量模型研究和讨论提供理论依据。（c）它们与关于具体化和扎根认知的辩论有何关系。我们确定了由于不完整的描述，过时的论点以及模型的理论和实现之间的不清楚而引起的常见误解。我们澄清和修改这些观点，为将来的语义表示向量模型研究和讨论提供理论依据。